Assessment of cardio function is vitally important in monitoring neonatal patients, burn or trauma patients, sleep studies, and other cases where a continuous measurement of the heart rate is required. Currently, cardiac pulse is measured using an electrocardiogram (ECG) which often requires adhesive patches, spring loaded clips, chest straps, and the like, which may prove uncomfortable to patients over the long term. The ability to monitor a patient's physiological signals by non-contact means is highly desirable in the healthcare industry. Although non-contact methods may not be able to provide details concerning cardiac electrical conduction that ECG offers, non-contact methods effectuate long term patient monitoring of various physiological signals such as heart rate by acquiring data in an unobtrusive manner. Such technology further minimizes wire, cabling, and the like, which tend to be associated with patient monitoring devices.
Photoplethysmography (PPG) is one non-invasive electro-optic technique which senses a cardiovascular pulse wave (also referred to as “blood volume pulse”) through variations in transmitted or reflected light. PPG provides valuable information about the cardiovascular system such as heart rate, arterial blood oxygen saturation, blood pressure, cardiac output, and autonomic function. PPG uses dedicated light sources but various studies have shown that pulse measurements can be acquired using normal ambient light as the illumination source. However, these efforts tend to rely on manual segmentation and heuristic interpretation of the captured raw images with minimal validation of performance characteristics. Furthermore, PPG is known to be susceptive to motion-induced signal corruption. In cases where the signal noise falls within the same frequency band as the physiological signal of interest, linear filtering with fixed cut-off frequencies can be rendered ineffective.
One technique for noise removal from physiological signals is blind source separation (BSS). BSS is a technique for the recovery of unobserved source signals from a set of observed mixed signals without any prior information being known about the “mixing” process. Typically, the observations are acquired from the output of a set of sensors where each sensor receives a different combination of source signals. Such methods acquire normal RGB signals from a CCD camera under normal ambient light and use BSS and Independent Component Analysis (ICA) to separate the source signals to detect pulse rate. Separating source signals using ICA on RGB signals can lead to errors due to the fact that the source can appear in any of the three outputs since the order in which the ICA returns the independent components will be random.
Accordingly, what is needed in this art are increasingly sophisticated systems and methods for recovering an estimated cardiac pulse rate from a sequence of RGB or multi-spectral video image data captured of a subject of interest being monitored for cardiac function in a remote sensing environment.